Handling Metadata in a Neurophysiology Laboratory.
Handling Metadata in a Neurophysiology Laboratory.
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DOI：
10.3389/fninf.2016.00026
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发表时间：
2016
影响因子：
3.5
通讯作者：
Grün S
中科院分区：
文献类型：
--
作者：
Zehl L;Jaillet F;Stoewer A;Grewe J;Sobolev A;Wachtler T;Brochier TG;Riehle A;Denker M;Grün S
迄今为止，神经生理学研究的不可复制性是科学界的激烈讨论的问题。增强可重复性的一个关键组件是全面收集和存储元数据，即有关实验，数据以及数据上所应用的预处理步骤的所有信息，以便它们可以以一致且简单的方式访问和共享。但是，实验的复杂性，高度专业化的分析工作流以及缺乏如何利用支持软件工具的知识，通常经常覆盖研究人员来执行此类详细的文档。因此，收集到的元数据通常是不完整的，对于局外人或模棱两可。根据我们在处理不同数据集的研究经验，我们在这里提供概念和技术指导，以克服与神经生理学实验室中元数据的收集，组织和存储相关的挑战。通过管理一个复杂实验的元数据的具体示例，该实验产生了执行行为运动任务的猴子的多渠道记录，我们实际上证明了这些方法和解决方案的实施，目的是将其推广到其他项目。此外，我们详细介绍了五种用例，这些用例证明了在处理或分析记录的数据时构建组织良好的元数据集合所带来的好处，特别是当实验室之间在现代科学协作中共享这些数据时。最后，我们建议使用ODML元数据框架从不同来源堆积，结构和存储元数据的适应性工作流程。
To date, non-reproducibility of neurophysiological research is a matter of intense discussion in the scientific community. A crucial component to enhance reproducibility is to comprehensively collect and store metadata, that is, all information about the experiment, the data, and the applied preprocessing steps on the data, such that they can be accessed and shared in a consistent and simple manner. However, the complexity of experiments, the highly specialized analysis workflows and a lack of knowledge on how to make use of supporting software tools often overburden researchers to perform such a detailed documentation. For this reason, the collected metadata are often incomplete, incomprehensible for outsiders or ambiguous. Based on our research experience in dealing with diverse datasets, we here provide conceptual and technical guidance to overcome the challenges associated with the collection, organization, and storage of metadata in a neurophysiology laboratory. Through the concrete example of managing the metadata of a complex experiment that yields multi-channel recordings from monkeys performing a behavioral motor task, we practically demonstrate the implementation of these approaches and solutions with the intention that they may be generalized to other projects. Moreover, we detail five use cases that demonstrate the resulting benefits of constructing a well-organized metadata collection when processing or analyzing the recorded data, in particular when these are shared between laboratories in a modern scientific collaboration. Finally, we suggest an adaptable workflow to accumulate, structure and store metadata from different sources using, by way of example, the odML metadata framework.